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Pattern Recognition and Image Analysis
Published in: Pattern Recognition and Image Analysis 4/2020

01-10-2020 | MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING

Image Classification by Mixed Finite Element Method and Orthogonal Legendre Moments

Authors: Amal Hjouji, Jaouad EL-Mekkaoui, Mosatafa Jourhmane

Published in: Pattern Recognition and Image Analysis | Issue 4/2020

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Abstract

In this paper, we propose a new classification system for image databases, particularly valid for noisy and geometrically distorted images. This system consists of three steps. In the first step, we apply a new image denoising technique based on the resolution of the Perona and Malik model using the finite element method (FEM). In the second step, we use the orthogonal invariant moments, applied to the obtained denoised images, to extract the feature vectors of images. In this step, we use a new set of orthogonal polynomials derived from the orthogonal Legendre polynomials, we call them orthogonal adapted-Legendre polynomials. These polynomials are used to define a series of orthogonal moments, which are invariant to translation, rotation, and scale. In the third steps, we use the radial basis function neural network (RBF), where the calculated feature vectors are the inputs of the input layer. To show the effectiveness of the proposed approach, we perform experimental tests and a comparative study with other well-known classification systems. The results obtained show the superiority and efficiency of our system.
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Metadata
Title
Image Classification by Mixed Finite Element Method and Orthogonal Legendre Moments
Authors
Amal Hjouji
Jaouad EL-Mekkaoui
Mosatafa Jourhmane
Publication date
01-10-2020
Publisher
Pleiades Publishing
Published in
Pattern Recognition and Image Analysis / Issue 4/2020
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI
https://doi.org/10.1134/S1054661820040185

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